{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8552e3dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'3.7.7 (tags/v3.7.7:d7c567b08f, Mar 10 2020, 10:41:24) [MSC v.1900 64 bit (AMD64)]'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import sys\n",
    "sys.version"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d77744cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'3.7.7'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import platform\n",
    "platform.python_version()"
   ]
  },
  {
   "cell_type": "raw",
   "id": "36f7b36d",
   "metadata": {},
   "source": [
    "除整形、浮点型、字符型这三种常规类型之外\n",
    "列表[]，字典{}，元组()，集合set或{}很常用\n",
    "\n",
    "列表：有序，可以存储任意类型，最常用\n",
    "字典：k, v 字典，任意格式，通过下标访问\n",
    "元组：有序，元素不可修改\n",
    "集合：元素不可重复"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3c14a8a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['a', 1, False, 1.0]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "is_list = ['a',1,False, 1.0]\n",
    "is_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "30d07c49",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'a': 1, 1: 'b'}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "is_dict = {'a':1, 1:'b'}\n",
    "is_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "582a24a1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 2, 1, 'a')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 与list的区别在于元素不可修改\n",
    "is_tuple = (1,2,1,'a')\n",
    "is_tuple"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6441aa66",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{1, 'a', 'b'}"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 集成也可以用{},或者用set\n",
    "is_set = {'a','b','a',1}\n",
    "is_set"
   ]
  },
  {
   "cell_type": "raw",
   "id": "8c03703c",
   "metadata": {},
   "source": [
    "numpy约束为可计算的，同一类型的元素，可以方便建立高维矩阵。\n",
    "python + numpy  = matlab的“矩阵计算”功能"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "86a694bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1.21.6'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "np.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6cb88672",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 1.],\n",
       "       [1., 1.],\n",
       "       [1., 1.]])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.ones([3,2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "42938287",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0.],\n",
       "       [0., 0.],\n",
       "       [0., 0.]])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.zeros([3,2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d39e0dac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0.],\n",
       "       [0., 0.],\n",
       "       [0., 0.]])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.empty([3,2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "599ca9e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2.12199579e-314, 0.00000000e+000],\n",
       "       [4.92089383e-321, 9.50654115e-312]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.ndarray([2,2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "5dc10a24",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>code</th>\n",
       "      <th>date</th>\n",
       "      <th>close</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>volume</th>\n",
       "      <th>amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>000300.SH</td>\n",
       "      <td>20220923</td>\n",
       "      <td>3856.0212</td>\n",
       "      <td>3865.1049</td>\n",
       "      <td>3888.3958</td>\n",
       "      <td>3829.6913</td>\n",
       "      <td>94342462.0</td>\n",
       "      <td>1.729048e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>000300.SH</td>\n",
       "      <td>20220922</td>\n",
       "      <td>3869.3440</td>\n",
       "      <td>3875.1771</td>\n",
       "      <td>3900.2736</td>\n",
       "      <td>3860.2308</td>\n",
       "      <td>80946919.0</td>\n",
       "      <td>1.611341e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>000300.SH</td>\n",
       "      <td>20220921</td>\n",
       "      <td>3903.7348</td>\n",
       "      <td>3921.3177</td>\n",
       "      <td>3924.3914</td>\n",
       "      <td>3884.2268</td>\n",
       "      <td>86458391.0</td>\n",
       "      <td>1.689391e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>000300.SH</td>\n",
       "      <td>20220920</td>\n",
       "      <td>3932.8361</td>\n",
       "      <td>3945.6467</td>\n",
       "      <td>3957.5538</td>\n",
       "      <td>3921.6884</td>\n",
       "      <td>84824134.0</td>\n",
       "      <td>1.792482e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>000300.SH</td>\n",
       "      <td>20220919</td>\n",
       "      <td>3928.0001</td>\n",
       "      <td>3928.4239</td>\n",
       "      <td>3953.6494</td>\n",
       "      <td>3910.3106</td>\n",
       "      <td>97719575.0</td>\n",
       "      <td>1.835744e+08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        code      date      close       open       high        low  \\\n",
       "0  000300.SH  20220923  3856.0212  3865.1049  3888.3958  3829.6913   \n",
       "1  000300.SH  20220922  3869.3440  3875.1771  3900.2736  3860.2308   \n",
       "2  000300.SH  20220921  3903.7348  3921.3177  3924.3914  3884.2268   \n",
       "3  000300.SH  20220920  3932.8361  3945.6467  3957.5538  3921.6884   \n",
       "4  000300.SH  20220919  3928.0001  3928.4239  3953.6494  3910.3106   \n",
       "\n",
       "       volume        amount  \n",
       "0  94342462.0  1.729048e+08  \n",
       "1  80946919.0  1.611341e+08  \n",
       "2  86458391.0  1.689391e+08  \n",
       "3  84824134.0  1.792482e+08  \n",
       "4  97719575.0  1.835744e+08  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv('../data/csv/000300.SH.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0e9738cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>code</th>\n",
       "      <th>date</th>\n",
       "      <th>close</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>volume</th>\n",
       "      <th>amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4304</th>\n",
       "      <td>000300.SH</td>\n",
       "      <td>20050110</td>\n",
       "      <td>993.879</td>\n",
       "      <td>983.760</td>\n",
       "      <td>993.959</td>\n",
       "      <td>979.789</td>\n",
       "      <td>5791697.99</td>\n",
       "      <td>3762932.890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4305</th>\n",
       "      <td>000300.SH</td>\n",
       "      <td>20050107</td>\n",
       "      <td>983.958</td>\n",
       "      <td>983.045</td>\n",
       "      <td>995.711</td>\n",
       "      <td>979.812</td>\n",
       "      <td>7298694.09</td>\n",
       "      <td>4737469.399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4306</th>\n",
       "      <td>000300.SH</td>\n",
       "      <td>20050106</td>\n",
       "      <td>983.174</td>\n",
       "      <td>993.331</td>\n",
       "      <td>993.788</td>\n",
       "      <td>980.330</td>\n",
       "      <td>6288029.05</td>\n",
       "      <td>3921015.420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4307</th>\n",
       "      <td>000300.SH</td>\n",
       "      <td>20050105</td>\n",
       "      <td>992.564</td>\n",
       "      <td>981.577</td>\n",
       "      <td>997.323</td>\n",
       "      <td>979.877</td>\n",
       "      <td>7119108.98</td>\n",
       "      <td>4529208.214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4308</th>\n",
       "      <td>000300.SH</td>\n",
       "      <td>20050104</td>\n",
       "      <td>982.794</td>\n",
       "      <td>994.769</td>\n",
       "      <td>994.769</td>\n",
       "      <td>980.658</td>\n",
       "      <td>7412868.94</td>\n",
       "      <td>4431977.418</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           code      date    close     open     high      low      volume  \\\n",
       "4304  000300.SH  20050110  993.879  983.760  993.959  979.789  5791697.99   \n",
       "4305  000300.SH  20050107  983.958  983.045  995.711  979.812  7298694.09   \n",
       "4306  000300.SH  20050106  983.174  993.331  993.788  980.330  6288029.05   \n",
       "4307  000300.SH  20050105  992.564  981.577  997.323  979.877  7119108.98   \n",
       "4308  000300.SH  20050104  982.794  994.769  994.769  980.658  7412868.94   \n",
       "\n",
       "           amount  \n",
       "4304  3762932.890  \n",
       "4305  4737469.399  \n",
       "4306  3921015.420  \n",
       "4307  4529208.214  \n",
       "4308  4431977.418  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c6eaf20f",
   "metadata": {},
   "outputs": [
    {
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       "      <th>amount</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2056</th>\n",
       "      <td>000300.SH</td>\n",
       "      <td>20140418</td>\n",
       "      <td>2224.4790</td>\n",
       "      <td>2215.8790</td>\n",
       "      <td>2228.7700</td>\n",
       "      <td>2203.0470</td>\n",
       "      <td>50757995.0</td>\n",
       "      <td>4.570962e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3193</th>\n",
       "      <td>000300.SH</td>\n",
       "      <td>20090806</td>\n",
       "      <td>3663.1200</td>\n",
       "      <td>3707.9070</td>\n",
       "      <td>3739.5780</td>\n",
       "      <td>3602.5750</td>\n",
       "      <td>118101525.0</td>\n",
       "      <td>1.784064e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1714</th>\n",
       "      <td>000300.SH</td>\n",
       "      <td>20150909</td>\n",
       "      <td>3399.3054</td>\n",
       "      <td>3344.8238</td>\n",
       "      <td>3426.6509</td>\n",
       "      <td>3330.2896</td>\n",
       "      <td>220467421.0</td>\n",
       "      <td>2.588158e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2398</th>\n",
       "      <td>000300.SH</td>\n",
       "      <td>20121114</td>\n",
       "      <td>2223.1100</td>\n",
       "      <td>2213.0860</td>\n",
       "      <td>2225.1240</td>\n",
       "      <td>2205.9960</td>\n",
       "      <td>26758790.0</td>\n",
       "      <td>2.359232e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2497</th>\n",
       "      <td>000300.SH</td>\n",
       "      <td>20120620</td>\n",
       "      <td>2552.6110</td>\n",
       "      <td>2563.6430</td>\n",
       "      <td>2570.6630</td>\n",
       "      <td>2549.4380</td>\n",
       "      <td>29850639.0</td>\n",
       "      <td>3.357367e+07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           code      date      close       open       high        low  \\\n",
       "2056  000300.SH  20140418  2224.4790  2215.8790  2228.7700  2203.0470   \n",
       "3193  000300.SH  20090806  3663.1200  3707.9070  3739.5780  3602.5750   \n",
       "1714  000300.SH  20150909  3399.3054  3344.8238  3426.6509  3330.2896   \n",
       "2398  000300.SH  20121114  2223.1100  2213.0860  2225.1240  2205.9960   \n",
       "2497  000300.SH  20120620  2552.6110  2563.6430  2570.6630  2549.4380   \n",
       "\n",
       "           volume        amount  \n",
       "2056   50757995.0  4.570962e+07  \n",
       "3193  118101525.0  1.784064e+08  \n",
       "1714  220467421.0  2.588158e+08  \n",
       "2398   26758790.0  2.359232e+07  \n",
       "2497   29850639.0  3.357367e+07  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "babacc8a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4309, 8)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ae547a20",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>code</th>\n",
       "      <th>date</th>\n",
       "      <th>close</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>volume</th>\n",
       "      <th>amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4304</th>\n",
       "      <td>000300.SH</td>\n",
       "      <td>20050110</td>\n",
       "      <td>993.879</td>\n",
       "      <td>983.760</td>\n",
       "      <td>993.959</td>\n",
       "      <td>979.789</td>\n",
       "      <td>5791697.99</td>\n",
       "      <td>3762932.890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4305</th>\n",
       "      <td>000300.SH</td>\n",
       "      <td>20050107</td>\n",
       "      <td>983.958</td>\n",
       "      <td>983.045</td>\n",
       "      <td>995.711</td>\n",
       "      <td>979.812</td>\n",
       "      <td>7298694.09</td>\n",
       "      <td>4737469.399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4306</th>\n",
       "      <td>000300.SH</td>\n",
       "      <td>20050106</td>\n",
       "      <td>983.174</td>\n",
       "      <td>993.331</td>\n",
       "      <td>993.788</td>\n",
       "      <td>980.330</td>\n",
       "      <td>6288029.05</td>\n",
       "      <td>3921015.420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4307</th>\n",
       "      <td>000300.SH</td>\n",
       "      <td>20050105</td>\n",
       "      <td>992.564</td>\n",
       "      <td>981.577</td>\n",
       "      <td>997.323</td>\n",
       "      <td>979.877</td>\n",
       "      <td>7119108.98</td>\n",
       "      <td>4529208.214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4308</th>\n",
       "      <td>000300.SH</td>\n",
       "      <td>20050104</td>\n",
       "      <td>982.794</td>\n",
       "      <td>994.769</td>\n",
       "      <td>994.769</td>\n",
       "      <td>980.658</td>\n",
       "      <td>7412868.94</td>\n",
       "      <td>4431977.418</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           code      date    close     open     high      low      volume  \\\n",
       "4304  000300.SH  20050110  993.879  983.760  993.959  979.789  5791697.99   \n",
       "4305  000300.SH  20050107  983.958  983.045  995.711  979.812  7298694.09   \n",
       "4306  000300.SH  20050106  983.174  993.331  993.788  980.330  6288029.05   \n",
       "4307  000300.SH  20050105  992.564  981.577  997.323  979.877  7119108.98   \n",
       "4308  000300.SH  20050104  982.794  994.769  994.769  980.658  7412868.94   \n",
       "\n",
       "           amount  \n",
       "4304  3762932.890  \n",
       "4305  4737469.399  \n",
       "4306  3921015.420  \n",
       "4307  4529208.214  \n",
       "4308  4431977.418  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "19bf7147",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>code</th>\n",
       "      <th>close</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>volume</th>\n",
       "      <th>amount</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20220923</th>\n",
       "      <td>000300.SH</td>\n",
       "      <td>3856.0212</td>\n",
       "      <td>3865.1049</td>\n",
       "      <td>3888.3958</td>\n",
       "      <td>3829.6913</td>\n",
       "      <td>94342462.00</td>\n",
       "      <td>1.729048e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20220922</th>\n",
       "      <td>000300.SH</td>\n",
       "      <td>3869.3440</td>\n",
       "      <td>3875.1771</td>\n",
       "      <td>3900.2736</td>\n",
       "      <td>3860.2308</td>\n",
       "      <td>80946919.00</td>\n",
       "      <td>1.611341e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20220921</th>\n",
       "      <td>000300.SH</td>\n",
       "      <td>3903.7348</td>\n",
       "      <td>3921.3177</td>\n",
       "      <td>3924.3914</td>\n",
       "      <td>3884.2268</td>\n",
       "      <td>86458391.00</td>\n",
       "      <td>1.689391e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20220920</th>\n",
       "      <td>000300.SH</td>\n",
       "      <td>3932.8361</td>\n",
       "      <td>3945.6467</td>\n",
       "      <td>3957.5538</td>\n",
       "      <td>3921.6884</td>\n",
       "      <td>84824134.00</td>\n",
       "      <td>1.792482e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20220919</th>\n",
       "      <td>000300.SH</td>\n",
       "      <td>3928.0001</td>\n",
       "      <td>3928.4239</td>\n",
       "      <td>3953.6494</td>\n",
       "      <td>3910.3106</td>\n",
       "      <td>97719575.00</td>\n",
       "      <td>1.835744e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20050110</th>\n",
       "      <td>000300.SH</td>\n",
       "      <td>993.8790</td>\n",
       "      <td>983.7600</td>\n",
       "      <td>993.9590</td>\n",
       "      <td>979.7890</td>\n",
       "      <td>5791697.99</td>\n",
       "      <td>3.762933e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20050107</th>\n",
       "      <td>000300.SH</td>\n",
       "      <td>983.9580</td>\n",
       "      <td>983.0450</td>\n",
       "      <td>995.7110</td>\n",
       "      <td>979.8120</td>\n",
       "      <td>7298694.09</td>\n",
       "      <td>4.737469e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20050106</th>\n",
       "      <td>000300.SH</td>\n",
       "      <td>983.1740</td>\n",
       "      <td>993.3310</td>\n",
       "      <td>993.7880</td>\n",
       "      <td>980.3300</td>\n",
       "      <td>6288029.05</td>\n",
       "      <td>3.921015e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20050105</th>\n",
       "      <td>000300.SH</td>\n",
       "      <td>992.5640</td>\n",
       "      <td>981.5770</td>\n",
       "      <td>997.3230</td>\n",
       "      <td>979.8770</td>\n",
       "      <td>7119108.98</td>\n",
       "      <td>4.529208e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20050104</th>\n",
       "      <td>000300.SH</td>\n",
       "      <td>982.7940</td>\n",
       "      <td>994.7690</td>\n",
       "      <td>994.7690</td>\n",
       "      <td>980.6580</td>\n",
       "      <td>7412868.94</td>\n",
       "      <td>4.431977e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4309 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               code      close       open       high        low       volume  \\\n",
       "date                                                                           \n",
       "20220923  000300.SH  3856.0212  3865.1049  3888.3958  3829.6913  94342462.00   \n",
       "20220922  000300.SH  3869.3440  3875.1771  3900.2736  3860.2308  80946919.00   \n",
       "20220921  000300.SH  3903.7348  3921.3177  3924.3914  3884.2268  86458391.00   \n",
       "20220920  000300.SH  3932.8361  3945.6467  3957.5538  3921.6884  84824134.00   \n",
       "20220919  000300.SH  3928.0001  3928.4239  3953.6494  3910.3106  97719575.00   \n",
       "...             ...        ...        ...        ...        ...          ...   \n",
       "20050110  000300.SH   993.8790   983.7600   993.9590   979.7890   5791697.99   \n",
       "20050107  000300.SH   983.9580   983.0450   995.7110   979.8120   7298694.09   \n",
       "20050106  000300.SH   983.1740   993.3310   993.7880   980.3300   6288029.05   \n",
       "20050105  000300.SH   992.5640   981.5770   997.3230   979.8770   7119108.98   \n",
       "20050104  000300.SH   982.7940   994.7690   994.7690   980.6580   7412868.94   \n",
       "\n",
       "                amount  \n",
       "date                    \n",
       "20220923  1.729048e+08  \n",
       "20220922  1.611341e+08  \n",
       "20220921  1.689391e+08  \n",
       "20220920  1.792482e+08  \n",
       "20220919  1.835744e+08  \n",
       "...                ...  \n",
       "20050110  3.762933e+06  \n",
       "20050107  4.737469e+06  \n",
       "20050106  3.921015e+06  \n",
       "20050105  4.529208e+06  \n",
       "20050104  4.431977e+06  \n",
       "\n",
       "[4309 rows x 7 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['date'] = df['date'].apply(lambda x:str(x))\n",
    "df.set_index('date',inplace=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "4ec8007c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>code</th>\n",
       "      <th>close</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>volume</th>\n",
       "      <th>amount</th>\n",
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       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20050104</th>\n",
       "      <td>000300.SH</td>\n",
       "      <td>982.7940</td>\n",
       "      <td>994.7690</td>\n",
       "      <td>994.7690</td>\n",
       "      <td>980.6580</td>\n",
       "      <td>7412868.94</td>\n",
       "      <td>4.431977e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20050105</th>\n",
       "      <td>000300.SH</td>\n",
       "      <td>992.5640</td>\n",
       "      <td>981.5770</td>\n",
       "      <td>997.3230</td>\n",
       "      <td>979.8770</td>\n",
       "      <td>7119108.98</td>\n",
       "      <td>4.529208e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20050106</th>\n",
       "      <td>000300.SH</td>\n",
       "      <td>983.1740</td>\n",
       "      <td>993.3310</td>\n",
       "      <td>993.7880</td>\n",
       "      <td>980.3300</td>\n",
       "      <td>6288029.05</td>\n",
       "      <td>3.921015e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20050107</th>\n",
       "      <td>000300.SH</td>\n",
       "      <td>983.9580</td>\n",
       "      <td>983.0450</td>\n",
       "      <td>995.7110</td>\n",
       "      <td>979.8120</td>\n",
       "      <td>7298694.09</td>\n",
       "      <td>4.737469e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20050110</th>\n",
       "      <td>000300.SH</td>\n",
       "      <td>993.8790</td>\n",
       "      <td>983.7600</td>\n",
       "      <td>993.9590</td>\n",
       "      <td>979.7890</td>\n",
       "      <td>5791697.99</td>\n",
       "      <td>3.762933e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20220919</th>\n",
       "      <td>000300.SH</td>\n",
       "      <td>3928.0001</td>\n",
       "      <td>3928.4239</td>\n",
       "      <td>3953.6494</td>\n",
       "      <td>3910.3106</td>\n",
       "      <td>97719575.00</td>\n",
       "      <td>1.835744e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20220920</th>\n",
       "      <td>000300.SH</td>\n",
       "      <td>3932.8361</td>\n",
       "      <td>3945.6467</td>\n",
       "      <td>3957.5538</td>\n",
       "      <td>3921.6884</td>\n",
       "      <td>84824134.00</td>\n",
       "      <td>1.792482e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20220921</th>\n",
       "      <td>000300.SH</td>\n",
       "      <td>3903.7348</td>\n",
       "      <td>3921.3177</td>\n",
       "      <td>3924.3914</td>\n",
       "      <td>3884.2268</td>\n",
       "      <td>86458391.00</td>\n",
       "      <td>1.689391e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20220922</th>\n",
       "      <td>000300.SH</td>\n",
       "      <td>3869.3440</td>\n",
       "      <td>3875.1771</td>\n",
       "      <td>3900.2736</td>\n",
       "      <td>3860.2308</td>\n",
       "      <td>80946919.00</td>\n",
       "      <td>1.611341e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20220923</th>\n",
       "      <td>000300.SH</td>\n",
       "      <td>3856.0212</td>\n",
       "      <td>3865.1049</td>\n",
       "      <td>3888.3958</td>\n",
       "      <td>3829.6913</td>\n",
       "      <td>94342462.00</td>\n",
       "      <td>1.729048e+08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4309 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               code      close       open       high        low       volume  \\\n",
       "date                                                                           \n",
       "20050104  000300.SH   982.7940   994.7690   994.7690   980.6580   7412868.94   \n",
       "20050105  000300.SH   992.5640   981.5770   997.3230   979.8770   7119108.98   \n",
       "20050106  000300.SH   983.1740   993.3310   993.7880   980.3300   6288029.05   \n",
       "20050107  000300.SH   983.9580   983.0450   995.7110   979.8120   7298694.09   \n",
       "20050110  000300.SH   993.8790   983.7600   993.9590   979.7890   5791697.99   \n",
       "...             ...        ...        ...        ...        ...          ...   \n",
       "20220919  000300.SH  3928.0001  3928.4239  3953.6494  3910.3106  97719575.00   \n",
       "20220920  000300.SH  3932.8361  3945.6467  3957.5538  3921.6884  84824134.00   \n",
       "20220921  000300.SH  3903.7348  3921.3177  3924.3914  3884.2268  86458391.00   \n",
       "20220922  000300.SH  3869.3440  3875.1771  3900.2736  3860.2308  80946919.00   \n",
       "20220923  000300.SH  3856.0212  3865.1049  3888.3958  3829.6913  94342462.00   \n",
       "\n",
       "                amount  \n",
       "date                    \n",
       "20050104  4.431977e+06  \n",
       "20050105  4.529208e+06  \n",
       "20050106  3.921015e+06  \n",
       "20050107  4.737469e+06  \n",
       "20050110  3.762933e+06  \n",
       "...                ...  \n",
       "20220919  1.835744e+08  \n",
       "20220920  1.792482e+08  \n",
       "20220921  1.689391e+08  \n",
       "20220922  1.611341e+08  \n",
       "20220923  1.729048e+08  \n",
       "\n",
       "[4309 rows x 7 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_index(ascending=True, inplace=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "5be600d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date\n",
       "20050104     982.7940\n",
       "20050105     992.5640\n",
       "20050106     983.1740\n",
       "20050107     983.9580\n",
       "20050110     993.8790\n",
       "              ...    \n",
       "20220919    3928.0001\n",
       "20220920    3932.8361\n",
       "20220921    3903.7348\n",
       "20220922    3869.3440\n",
       "20220923    3856.0212\n",
       "Name: close, Length: 4309, dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#取收盘价\n",
    "df['close']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "c19b3276",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>close</th>\n",
       "      <th>open</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20050104</th>\n",
       "      <td>982.7940</td>\n",
       "      <td>994.7690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20050105</th>\n",
       "      <td>992.5640</td>\n",
       "      <td>981.5770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20050106</th>\n",
       "      <td>983.1740</td>\n",
       "      <td>993.3310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20050107</th>\n",
       "      <td>983.9580</td>\n",
       "      <td>983.0450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20050110</th>\n",
       "      <td>993.8790</td>\n",
       "      <td>983.7600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20220919</th>\n",
       "      <td>3928.0001</td>\n",
       "      <td>3928.4239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20220920</th>\n",
       "      <td>3932.8361</td>\n",
       "      <td>3945.6467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20220921</th>\n",
       "      <td>3903.7348</td>\n",
       "      <td>3921.3177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20220922</th>\n",
       "      <td>3869.3440</td>\n",
       "      <td>3875.1771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20220923</th>\n",
       "      <td>3856.0212</td>\n",
       "      <td>3865.1049</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4309 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              close       open\n",
       "date                          \n",
       "20050104   982.7940   994.7690\n",
       "20050105   992.5640   981.5770\n",
       "20050106   983.1740   993.3310\n",
       "20050107   983.9580   983.0450\n",
       "20050110   993.8790   983.7600\n",
       "...             ...        ...\n",
       "20220919  3928.0001  3928.4239\n",
       "20220920  3932.8361  3945.6467\n",
       "20220921  3903.7348  3921.3177\n",
       "20220922  3869.3440  3875.1771\n",
       "20220923  3856.0212  3865.1049\n",
       "\n",
       "[4309 rows x 2 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 取收盘价，开盘价\n",
    "df[['close','open']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "1dd692f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>code</th>\n",
       "      <th>close</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>volume</th>\n",
       "      <th>amount</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20050104</th>\n",
       "      <td>000300.SH</td>\n",
       "      <td>982.794</td>\n",
       "      <td>994.769</td>\n",
       "      <td>994.769</td>\n",
       "      <td>980.658</td>\n",
       "      <td>7412868.94</td>\n",
       "      <td>4431977.418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20050105</th>\n",
       "      <td>000300.SH</td>\n",
       "      <td>992.564</td>\n",
       "      <td>981.577</td>\n",
       "      <td>997.323</td>\n",
       "      <td>979.877</td>\n",
       "      <td>7119108.98</td>\n",
       "      <td>4529208.214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20050106</th>\n",
       "      <td>000300.SH</td>\n",
       "      <td>983.174</td>\n",
       "      <td>993.331</td>\n",
       "      <td>993.788</td>\n",
       "      <td>980.330</td>\n",
       "      <td>6288029.05</td>\n",
       "      <td>3921015.420</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               code    close     open     high      low      volume  \\\n",
       "date                                                                  \n",
       "20050104  000300.SH  982.794  994.769  994.769  980.658  7412868.94   \n",
       "20050105  000300.SH  992.564  981.577  997.323  979.877  7119108.98   \n",
       "20050106  000300.SH  983.174  993.331  993.788  980.330  6288029.05   \n",
       "\n",
       "               amount  \n",
       "date                   \n",
       "20050104  4431977.418  \n",
       "20050105  4529208.214  \n",
       "20050106  3921015.420  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#取行子集, 左闭右开 ，等价于 df[0:3]\n",
    "df[:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "51efca16",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['20050104', '20050105', '20050106', '20050107', '20050110', '20050111',\n",
       "       '20050112', '20050113', '20050114', '20050117',\n",
       "       ...\n",
       "       '20220909', '20220913', '20220914', '20220915', '20220916', '20220919',\n",
       "       '20220920', '20220921', '20220922', '20220923'],\n",
       "      dtype='object', name='date', length=4309)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "193a2920",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>code</th>\n",
       "      <th>close</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>volume</th>\n",
       "      <th>amount</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20220919</th>\n",
       "      <td>000300.SH</td>\n",
       "      <td>3928.0001</td>\n",
       "      <td>3928.4239</td>\n",
       "      <td>3953.6494</td>\n",
       "      <td>3910.3106</td>\n",
       "      <td>97719575.0</td>\n",
       "      <td>1.835744e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20220920</th>\n",
       "      <td>000300.SH</td>\n",
       "      <td>3932.8361</td>\n",
       "      <td>3945.6467</td>\n",
       "      <td>3957.5538</td>\n",
       "      <td>3921.6884</td>\n",
       "      <td>84824134.0</td>\n",
       "      <td>1.792482e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20220921</th>\n",
       "      <td>000300.SH</td>\n",
       "      <td>3903.7348</td>\n",
       "      <td>3921.3177</td>\n",
       "      <td>3924.3914</td>\n",
       "      <td>3884.2268</td>\n",
       "      <td>86458391.0</td>\n",
       "      <td>1.689391e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20220922</th>\n",
       "      <td>000300.SH</td>\n",
       "      <td>3869.3440</td>\n",
       "      <td>3875.1771</td>\n",
       "      <td>3900.2736</td>\n",
       "      <td>3860.2308</td>\n",
       "      <td>80946919.0</td>\n",
       "      <td>1.611341e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20220923</th>\n",
       "      <td>000300.SH</td>\n",
       "      <td>3856.0212</td>\n",
       "      <td>3865.1049</td>\n",
       "      <td>3888.3958</td>\n",
       "      <td>3829.6913</td>\n",
       "      <td>94342462.0</td>\n",
       "      <td>1.729048e+08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               code      close       open       high        low      volume  \\\n",
       "date                                                                          \n",
       "20220919  000300.SH  3928.0001  3928.4239  3953.6494  3910.3106  97719575.0   \n",
       "20220920  000300.SH  3932.8361  3945.6467  3957.5538  3921.6884  84824134.0   \n",
       "20220921  000300.SH  3903.7348  3921.3177  3924.3914  3884.2268  86458391.0   \n",
       "20220922  000300.SH  3869.3440  3875.1771  3900.2736  3860.2308  80946919.0   \n",
       "20220923  000300.SH  3856.0212  3865.1049  3888.3958  3829.6913  94342462.0   \n",
       "\n",
       "                amount  \n",
       "date                    \n",
       "20220919  1.835744e+08  \n",
       "20220920  1.792482e+08  \n",
       "20220921  1.689391e+08  \n",
       "20220922  1.611341e+08  \n",
       "20220923  1.729048e+08  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#loc是从索引取直接取，左闭右闭\n",
    "df.loc['20220919':'20220923']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "9ce67893",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20050104</th>\n",
       "      <td>000300.SH</td>\n",
       "      <td>982.794</td>\n",
       "      <td>994.769</td>\n",
       "      <td>994.769</td>\n",
       "      <td>980.658</td>\n",
       "      <td>7412868.94</td>\n",
       "      <td>4431977.418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20050105</th>\n",
       "      <td>000300.SH</td>\n",
       "      <td>992.564</td>\n",
       "      <td>981.577</td>\n",
       "      <td>997.323</td>\n",
       "      <td>979.877</td>\n",
       "      <td>7119108.98</td>\n",
       "      <td>4529208.214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20050106</th>\n",
       "      <td>000300.SH</td>\n",
       "      <td>983.174</td>\n",
       "      <td>993.331</td>\n",
       "      <td>993.788</td>\n",
       "      <td>980.330</td>\n",
       "      <td>6288029.05</td>\n",
       "      <td>3921015.420</td>\n",
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      ],
      "text/plain": [
       "               code    close     open     high      low      volume  \\\n",
       "date                                                                  \n",
       "20050104  000300.SH  982.794  994.769  994.769  980.658  7412868.94   \n",
       "20050105  000300.SH  992.564  981.577  997.323  979.877  7119108.98   \n",
       "20050106  000300.SH  983.174  993.331  993.788  980.330  6288029.05   \n",
       "\n",
       "               amount  \n",
       "date                   \n",
       "20050104  4431977.418  \n",
       "20050105  4529208.214  \n",
       "20050106  3921015.420  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#iloc是按下标走，左闭左开，切片\n",
    "df.iloc[0:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "76c03cd1",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>code</th>\n",
       "      <th>close</th>\n",
       "      <th>open</th>\n",
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       "      <th>low</th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20220921</th>\n",
       "      <td>000300.SH</td>\n",
       "      <td>3903.7348</td>\n",
       "      <td>3921.3177</td>\n",
       "      <td>3924.3914</td>\n",
       "      <td>3884.2268</td>\n",
       "      <td>86458391.0</td>\n",
       "      <td>1.689391e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20220922</th>\n",
       "      <td>000300.SH</td>\n",
       "      <td>3869.3440</td>\n",
       "      <td>3875.1771</td>\n",
       "      <td>3900.2736</td>\n",
       "      <td>3860.2308</td>\n",
       "      <td>80946919.0</td>\n",
       "      <td>1.611341e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20220923</th>\n",
       "      <td>000300.SH</td>\n",
       "      <td>3856.0212</td>\n",
       "      <td>3865.1049</td>\n",
       "      <td>3888.3958</td>\n",
       "      <td>3829.6913</td>\n",
       "      <td>94342462.0</td>\n",
       "      <td>1.729048e+08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               code      close       open       high        low      volume  \\\n",
       "date                                                                          \n",
       "20220921  000300.SH  3903.7348  3921.3177  3924.3914  3884.2268  86458391.0   \n",
       "20220922  000300.SH  3869.3440  3875.1771  3900.2736  3860.2308  80946919.0   \n",
       "20220923  000300.SH  3856.0212  3865.1049  3888.3958  3829.6913  94342462.0   \n",
       "\n",
       "                amount  \n",
       "date                    \n",
       "20220921  1.689391e+08  \n",
       "20220922  1.611341e+08  \n",
       "20220923  1.729048e+08  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#iloc是按下标走，左闭左开，切片\n",
    "df.iloc[-3:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "69e573b4",
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>code</th>\n",
       "      <th>close</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>volume</th>\n",
       "      <th>amount</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20220922</th>\n",
       "      <td>000300.SH</td>\n",
       "      <td>3869.344</td>\n",
       "      <td>3875.1771</td>\n",
       "      <td>3900.2736</td>\n",
       "      <td>3860.2308</td>\n",
       "      <td>80946919.0</td>\n",
       "      <td>1.611341e+08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               code     close       open       high        low      volume  \\\n",
       "date                                                                         \n",
       "20220922  000300.SH  3869.344  3875.1771  3900.2736  3860.2308  80946919.0   \n",
       "\n",
       "                amount  \n",
       "date                    \n",
       "20220922  1.611341e+08  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.index=='20220922']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "0e53a939",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>close</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20220921</th>\n",
       "      <td>3903.7348</td>\n",
       "      <td>3921.3177</td>\n",
       "      <td>3924.3914</td>\n",
       "      <td>3884.2268</td>\n",
       "      <td>86458391.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20220922</th>\n",
       "      <td>3869.3440</td>\n",
       "      <td>3875.1771</td>\n",
       "      <td>3900.2736</td>\n",
       "      <td>3860.2308</td>\n",
       "      <td>80946919.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20220923</th>\n",
       "      <td>3856.0212</td>\n",
       "      <td>3865.1049</td>\n",
       "      <td>3888.3958</td>\n",
       "      <td>3829.6913</td>\n",
       "      <td>94342462.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              close       open       high        low      volume\n",
       "date                                                            \n",
       "20220921  3903.7348  3921.3177  3924.3914  3884.2268  86458391.0\n",
       "20220922  3869.3440  3875.1771  3900.2736  3860.2308  80946919.0\n",
       "20220923  3856.0212  3865.1049  3888.3958  3829.6913  94342462.0"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 同时指定行和列\n",
    "df.loc['20220921':'20220923','close':'volume']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "44a2e18a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>code</th>\n",
       "      <th>close</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>volume</th>\n",
       "      <th>amount</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20070528</th>\n",
       "      <td>000300.SH</td>\n",
       "      <td>4072.578</td>\n",
       "      <td>4033.921</td>\n",
       "      <td>4091.935</td>\n",
       "      <td>4027.363</td>\n",
       "      <td>114041851.0</td>\n",
       "      <td>1.832129e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20070529</th>\n",
       "      <td>000300.SH</td>\n",
       "      <td>4168.289</td>\n",
       "      <td>4090.903</td>\n",
       "      <td>4168.529</td>\n",
       "      <td>4079.868</td>\n",
       "      <td>113267227.0</td>\n",
       "      <td>1.794767e+08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               code     close      open      high       low       volume  \\\n",
       "date                                                                       \n",
       "20070528  000300.SH  4072.578  4033.921  4091.935  4027.363  114041851.0   \n",
       "20070529  000300.SH  4168.289  4090.903  4168.529  4079.868  113267227.0   \n",
       "\n",
       "                amount  \n",
       "date                    \n",
       "20070528  1.832129e+08  \n",
       "20070529  1.794767e+08  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 收盘价 大于 3900点\n",
    "df[df['close']>4000].iloc[0:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "488f4340",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>code</th>\n",
       "      <th>close</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>volume</th>\n",
       "      <th>amount</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20050603</th>\n",
       "      <td>000300.SH</td>\n",
       "      <td>818.0330</td>\n",
       "      <td>816.5460</td>\n",
       "      <td>823.8600</td>\n",
       "      <td>807.9650</td>\n",
       "      <td>7936056.0</td>\n",
       "      <td>4.164754e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20050602</th>\n",
       "      <td>000300.SH</td>\n",
       "      <td>818.3790</td>\n",
       "      <td>835.4670</td>\n",
       "      <td>835.4930</td>\n",
       "      <td>812.9890</td>\n",
       "      <td>9508657.0</td>\n",
       "      <td>5.220786e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20050711</th>\n",
       "      <td>000300.SH</td>\n",
       "      <td>824.0970</td>\n",
       "      <td>837.8630</td>\n",
       "      <td>850.6580</td>\n",
       "      <td>822.5240</td>\n",
       "      <td>8439006.0</td>\n",
       "      <td>4.092654e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20050708</th>\n",
       "      <td>000300.SH</td>\n",
       "      <td>829.4890</td>\n",
       "      <td>842.6200</td>\n",
       "      <td>842.6200</td>\n",
       "      <td>827.2290</td>\n",
       "      <td>7221000.0</td>\n",
       "      <td>3.447787e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20050718</th>\n",
       "      <td>000300.SH</td>\n",
       "      <td>832.9950</td>\n",
       "      <td>839.2210</td>\n",
       "      <td>840.0050</td>\n",
       "      <td>829.6790</td>\n",
       "      <td>8500006.0</td>\n",
       "      <td>3.767755e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20210219</th>\n",
       "      <td>000300.SH</td>\n",
       "      <td>5778.8420</td>\n",
       "      <td>5734.0193</td>\n",
       "      <td>5787.8871</td>\n",
       "      <td>5667.3209</td>\n",
       "      <td>214475780.0</td>\n",
       "      <td>4.564338e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20210210</th>\n",
       "      <td>000300.SH</td>\n",
       "      <td>5807.7191</td>\n",
       "      <td>5709.6840</td>\n",
       "      <td>5823.4219</td>\n",
       "      <td>5707.3295</td>\n",
       "      <td>170169786.0</td>\n",
       "      <td>4.170565e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20071015</th>\n",
       "      <td>000300.SH</td>\n",
       "      <td>5821.4470</td>\n",
       "      <td>5761.8690</td>\n",
       "      <td>5824.9760</td>\n",
       "      <td>5690.0700</td>\n",
       "      <td>70682111.0</td>\n",
       "      <td>1.550521e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20071017</th>\n",
       "      <td>000300.SH</td>\n",
       "      <td>5824.1170</td>\n",
       "      <td>5862.3780</td>\n",
       "      <td>5891.7230</td>\n",
       "      <td>5797.6110</td>\n",
       "      <td>48869768.0</td>\n",
       "      <td>1.083284e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20071016</th>\n",
       "      <td>000300.SH</td>\n",
       "      <td>5877.2020</td>\n",
       "      <td>5851.5380</td>\n",
       "      <td>5885.4840</td>\n",
       "      <td>5815.6090</td>\n",
       "      <td>64692049.0</td>\n",
       "      <td>1.350923e+08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4309 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               code      close       open       high        low       volume  \\\n",
       "date                                                                           \n",
       "20050603  000300.SH   818.0330   816.5460   823.8600   807.9650    7936056.0   \n",
       "20050602  000300.SH   818.3790   835.4670   835.4930   812.9890    9508657.0   \n",
       "20050711  000300.SH   824.0970   837.8630   850.6580   822.5240    8439006.0   \n",
       "20050708  000300.SH   829.4890   842.6200   842.6200   827.2290    7221000.0   \n",
       "20050718  000300.SH   832.9950   839.2210   840.0050   829.6790    8500006.0   \n",
       "...             ...        ...        ...        ...        ...          ...   \n",
       "20210219  000300.SH  5778.8420  5734.0193  5787.8871  5667.3209  214475780.0   \n",
       "20210210  000300.SH  5807.7191  5709.6840  5823.4219  5707.3295  170169786.0   \n",
       "20071015  000300.SH  5821.4470  5761.8690  5824.9760  5690.0700   70682111.0   \n",
       "20071017  000300.SH  5824.1170  5862.3780  5891.7230  5797.6110   48869768.0   \n",
       "20071016  000300.SH  5877.2020  5851.5380  5885.4840  5815.6090   64692049.0   \n",
       "\n",
       "                amount  \n",
       "date                    \n",
       "20050603  4.164754e+06  \n",
       "20050602  5.220786e+06  \n",
       "20050711  4.092654e+06  \n",
       "20050708  3.447787e+06  \n",
       "20050718  3.767755e+06  \n",
       "...                ...  \n",
       "20210219  4.564338e+08  \n",
       "20210210  4.170565e+08  \n",
       "20071015  1.550521e+08  \n",
       "20071017  1.083284e+08  \n",
       "20071016  1.350923e+08  \n",
       "\n",
       "[4309 rows x 7 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 排序\n",
    "df.sort_values('close',ascending=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "8ae53bc3",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>code</th>\n",
       "      <th>close</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>volume</th>\n",
       "      <th>amount</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20050603</th>\n",
       "      <td>000300.SH</td>\n",
       "      <td>818.0330</td>\n",
       "      <td>816.5460</td>\n",
       "      <td>823.8600</td>\n",
       "      <td>807.9650</td>\n",
       "      <td>7936056.0</td>\n",
       "      <td>4.164754e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20050602</th>\n",
       "      <td>000300.SH</td>\n",
       "      <td>818.3790</td>\n",
       "      <td>835.4670</td>\n",
       "      <td>835.4930</td>\n",
       "      <td>812.9890</td>\n",
       "      <td>9508657.0</td>\n",
       "      <td>5.220786e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20050711</th>\n",
       "      <td>000300.SH</td>\n",
       "      <td>824.0970</td>\n",
       "      <td>837.8630</td>\n",
       "      <td>850.6580</td>\n",
       "      <td>822.5240</td>\n",
       "      <td>8439006.0</td>\n",
       "      <td>4.092654e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20050708</th>\n",
       "      <td>000300.SH</td>\n",
       "      <td>829.4890</td>\n",
       "      <td>842.6200</td>\n",
       "      <td>842.6200</td>\n",
       "      <td>827.2290</td>\n",
       "      <td>7221000.0</td>\n",
       "      <td>3.447787e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20050718</th>\n",
       "      <td>000300.SH</td>\n",
       "      <td>832.9950</td>\n",
       "      <td>839.2210</td>\n",
       "      <td>840.0050</td>\n",
       "      <td>829.6790</td>\n",
       "      <td>8500006.0</td>\n",
       "      <td>3.767755e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20210219</th>\n",
       "      <td>000300.SH</td>\n",
       "      <td>5778.8420</td>\n",
       "      <td>5734.0193</td>\n",
       "      <td>5787.8871</td>\n",
       "      <td>5667.3209</td>\n",
       "      <td>214475780.0</td>\n",
       "      <td>4.564338e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20210210</th>\n",
       "      <td>000300.SH</td>\n",
       "      <td>5807.7191</td>\n",
       "      <td>5709.6840</td>\n",
       "      <td>5823.4219</td>\n",
       "      <td>5707.3295</td>\n",
       "      <td>170169786.0</td>\n",
       "      <td>4.170565e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20071015</th>\n",
       "      <td>000300.SH</td>\n",
       "      <td>5821.4470</td>\n",
       "      <td>5761.8690</td>\n",
       "      <td>5824.9760</td>\n",
       "      <td>5690.0700</td>\n",
       "      <td>70682111.0</td>\n",
       "      <td>1.550521e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20071017</th>\n",
       "      <td>000300.SH</td>\n",
       "      <td>5824.1170</td>\n",
       "      <td>5862.3780</td>\n",
       "      <td>5891.7230</td>\n",
       "      <td>5797.6110</td>\n",
       "      <td>48869768.0</td>\n",
       "      <td>1.083284e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20071016</th>\n",
       "      <td>000300.SH</td>\n",
       "      <td>5877.2020</td>\n",
       "      <td>5851.5380</td>\n",
       "      <td>5885.4840</td>\n",
       "      <td>5815.6090</td>\n",
       "      <td>64692049.0</td>\n",
       "      <td>1.350923e+08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4309 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               code      close       open       high        low       volume  \\\n",
       "date                                                                           \n",
       "20050603  000300.SH   818.0330   816.5460   823.8600   807.9650    7936056.0   \n",
       "20050602  000300.SH   818.3790   835.4670   835.4930   812.9890    9508657.0   \n",
       "20050711  000300.SH   824.0970   837.8630   850.6580   822.5240    8439006.0   \n",
       "20050708  000300.SH   829.4890   842.6200   842.6200   827.2290    7221000.0   \n",
       "20050718  000300.SH   832.9950   839.2210   840.0050   829.6790    8500006.0   \n",
       "...             ...        ...        ...        ...        ...          ...   \n",
       "20210219  000300.SH  5778.8420  5734.0193  5787.8871  5667.3209  214475780.0   \n",
       "20210210  000300.SH  5807.7191  5709.6840  5823.4219  5707.3295  170169786.0   \n",
       "20071015  000300.SH  5821.4470  5761.8690  5824.9760  5690.0700   70682111.0   \n",
       "20071017  000300.SH  5824.1170  5862.3780  5891.7230  5797.6110   48869768.0   \n",
       "20071016  000300.SH  5877.2020  5851.5380  5885.4840  5815.6090   64692049.0   \n",
       "\n",
       "                amount  \n",
       "date                    \n",
       "20050603  4.164754e+06  \n",
       "20050602  5.220786e+06  \n",
       "20050711  4.092654e+06  \n",
       "20050708  3.447787e+06  \n",
       "20050718  3.767755e+06  \n",
       "...                ...  \n",
       "20210219  4.564338e+08  \n",
       "20210210  4.170565e+08  \n",
       "20071015  1.550521e+08  \n",
       "20071017  1.083284e+08  \n",
       "20071016  1.350923e+08  \n",
       "\n",
       "[4309 rows x 7 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 排序\n",
    "df.sort_values(['close','open'],ascending=[True,False])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "5862d547",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>volume</th>\n",
       "      <th>amount</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>close</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>818.0330</th>\n",
       "      <td>816.5460</td>\n",
       "      <td>823.8600</td>\n",
       "      <td>807.9650</td>\n",
       "      <td>7936056.0</td>\n",
       "      <td>4.164754e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>818.3790</th>\n",
       "      <td>835.4670</td>\n",
       "      <td>835.4930</td>\n",
       "      <td>812.9890</td>\n",
       "      <td>9508657.0</td>\n",
       "      <td>5.220786e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>824.0970</th>\n",
       "      <td>837.8630</td>\n",
       "      <td>850.6580</td>\n",
       "      <td>822.5240</td>\n",
       "      <td>8439006.0</td>\n",
       "      <td>4.092654e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>829.4890</th>\n",
       "      <td>842.6200</td>\n",
       "      <td>842.6200</td>\n",
       "      <td>827.2290</td>\n",
       "      <td>7221000.0</td>\n",
       "      <td>3.447787e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>832.9950</th>\n",
       "      <td>839.2210</td>\n",
       "      <td>840.0050</td>\n",
       "      <td>829.6790</td>\n",
       "      <td>8500006.0</td>\n",
       "      <td>3.767755e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5778.8420</th>\n",
       "      <td>5734.0193</td>\n",
       "      <td>5787.8871</td>\n",
       "      <td>5667.3209</td>\n",
       "      <td>214475780.0</td>\n",
       "      <td>4.564338e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5807.7191</th>\n",
       "      <td>5709.6840</td>\n",
       "      <td>5823.4219</td>\n",
       "      <td>5707.3295</td>\n",
       "      <td>170169786.0</td>\n",
       "      <td>4.170565e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5821.4470</th>\n",
       "      <td>5761.8690</td>\n",
       "      <td>5824.9760</td>\n",
       "      <td>5690.0700</td>\n",
       "      <td>70682111.0</td>\n",
       "      <td>1.550521e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5824.1170</th>\n",
       "      <td>5862.3780</td>\n",
       "      <td>5891.7230</td>\n",
       "      <td>5797.6110</td>\n",
       "      <td>48869768.0</td>\n",
       "      <td>1.083284e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5877.2020</th>\n",
       "      <td>5851.5380</td>\n",
       "      <td>5885.4840</td>\n",
       "      <td>5815.6090</td>\n",
       "      <td>64692049.0</td>\n",
       "      <td>1.350923e+08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4309 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                open       high        low       volume        amount\n",
       "close                                                                \n",
       "818.0330    816.5460   823.8600   807.9650    7936056.0  4.164754e+06\n",
       "818.3790    835.4670   835.4930   812.9890    9508657.0  5.220786e+06\n",
       "824.0970    837.8630   850.6580   822.5240    8439006.0  4.092654e+06\n",
       "829.4890    842.6200   842.6200   827.2290    7221000.0  3.447787e+06\n",
       "832.9950    839.2210   840.0050   829.6790    8500006.0  3.767755e+06\n",
       "...              ...        ...        ...          ...           ...\n",
       "5778.8420  5734.0193  5787.8871  5667.3209  214475780.0  4.564338e+08\n",
       "5807.7191  5709.6840  5823.4219  5707.3295  170169786.0  4.170565e+08\n",
       "5821.4470  5761.8690  5824.9760  5690.0700   70682111.0  1.550521e+08\n",
       "5824.1170  5862.3780  5891.7230  5797.6110   48869768.0  1.083284e+08\n",
       "5877.2020  5851.5380  5885.4840  5815.6090   64692049.0  1.350923e+08\n",
       "\n",
       "[4309 rows x 5 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#分组聚合\n",
    "df.groupby('close').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "cf511507",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c06d0a4fc8>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#df.index = pd.to_datetime(df.index)\n",
    "df[['low','high']].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "00f9a162",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c06f78c188>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['rate'] = df['close'].pct_change()\n",
    "df['rate'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "11999376",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>code</th>\n",
       "      <th>close</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>volume</th>\n",
       "      <th>amount</th>\n",
       "      <th>rate</th>\n",
       "      <th>rate2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20050104</th>\n",
       "      <td>000300.SH</td>\n",
       "      <td>982.7940</td>\n",
       "      <td>994.7690</td>\n",
       "      <td>994.7690</td>\n",
       "      <td>980.6580</td>\n",
       "      <td>7412868.94</td>\n",
       "      <td>4.431977e+06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20050105</th>\n",
       "      <td>000300.SH</td>\n",
       "      <td>992.5640</td>\n",
       "      <td>981.5770</td>\n",
       "      <td>997.3230</td>\n",
       "      <td>979.8770</td>\n",
       "      <td>7119108.98</td>\n",
       "      <td>4.529208e+06</td>\n",
       "      <td>0.009941</td>\n",
       "      <td>0.009941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20050106</th>\n",
       "      <td>000300.SH</td>\n",
       "      <td>983.1740</td>\n",
       "      <td>993.3310</td>\n",
       "      <td>993.7880</td>\n",
       "      <td>980.3300</td>\n",
       "      <td>6288029.05</td>\n",
       "      <td>3.921015e+06</td>\n",
       "      <td>-0.009460</td>\n",
       "      <td>-0.009460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20050107</th>\n",
       "      <td>000300.SH</td>\n",
       "      <td>983.9580</td>\n",
       "      <td>983.0450</td>\n",
       "      <td>995.7110</td>\n",
       "      <td>979.8120</td>\n",
       "      <td>7298694.09</td>\n",
       "      <td>4.737469e+06</td>\n",
       "      <td>0.000797</td>\n",
       "      <td>0.000797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20050110</th>\n",
       "      <td>000300.SH</td>\n",
       "      <td>993.8790</td>\n",
       "      <td>983.7600</td>\n",
       "      <td>993.9590</td>\n",
       "      <td>979.7890</td>\n",
       "      <td>5791697.99</td>\n",
       "      <td>3.762933e+06</td>\n",
       "      <td>0.010083</td>\n",
       "      <td>0.010083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20220919</th>\n",
       "      <td>000300.SH</td>\n",
       "      <td>3928.0001</td>\n",
       "      <td>3928.4239</td>\n",
       "      <td>3953.6494</td>\n",
       "      <td>3910.3106</td>\n",
       "      <td>97719575.00</td>\n",
       "      <td>1.835744e+08</td>\n",
       "      <td>-0.001191</td>\n",
       "      <td>-0.001191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20220920</th>\n",
       "      <td>000300.SH</td>\n",
       "      <td>3932.8361</td>\n",
       "      <td>3945.6467</td>\n",
       "      <td>3957.5538</td>\n",
       "      <td>3921.6884</td>\n",
       "      <td>84824134.00</td>\n",
       "      <td>1.792482e+08</td>\n",
       "      <td>0.001231</td>\n",
       "      <td>0.001231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20220921</th>\n",
       "      <td>000300.SH</td>\n",
       "      <td>3903.7348</td>\n",
       "      <td>3921.3177</td>\n",
       "      <td>3924.3914</td>\n",
       "      <td>3884.2268</td>\n",
       "      <td>86458391.00</td>\n",
       "      <td>1.689391e+08</td>\n",
       "      <td>-0.007400</td>\n",
       "      <td>-0.007400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20220922</th>\n",
       "      <td>000300.SH</td>\n",
       "      <td>3869.3440</td>\n",
       "      <td>3875.1771</td>\n",
       "      <td>3900.2736</td>\n",
       "      <td>3860.2308</td>\n",
       "      <td>80946919.00</td>\n",
       "      <td>1.611341e+08</td>\n",
       "      <td>-0.008810</td>\n",
       "      <td>-0.008810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20220923</th>\n",
       "      <td>000300.SH</td>\n",
       "      <td>3856.0212</td>\n",
       "      <td>3865.1049</td>\n",
       "      <td>3888.3958</td>\n",
       "      <td>3829.6913</td>\n",
       "      <td>94342462.00</td>\n",
       "      <td>1.729048e+08</td>\n",
       "      <td>-0.003443</td>\n",
       "      <td>-0.003443</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4309 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               code      close       open       high        low       volume  \\\n",
       "date                                                                           \n",
       "20050104  000300.SH   982.7940   994.7690   994.7690   980.6580   7412868.94   \n",
       "20050105  000300.SH   992.5640   981.5770   997.3230   979.8770   7119108.98   \n",
       "20050106  000300.SH   983.1740   993.3310   993.7880   980.3300   6288029.05   \n",
       "20050107  000300.SH   983.9580   983.0450   995.7110   979.8120   7298694.09   \n",
       "20050110  000300.SH   993.8790   983.7600   993.9590   979.7890   5791697.99   \n",
       "...             ...        ...        ...        ...        ...          ...   \n",
       "20220919  000300.SH  3928.0001  3928.4239  3953.6494  3910.3106  97719575.00   \n",
       "20220920  000300.SH  3932.8361  3945.6467  3957.5538  3921.6884  84824134.00   \n",
       "20220921  000300.SH  3903.7348  3921.3177  3924.3914  3884.2268  86458391.00   \n",
       "20220922  000300.SH  3869.3440  3875.1771  3900.2736  3860.2308  80946919.00   \n",
       "20220923  000300.SH  3856.0212  3865.1049  3888.3958  3829.6913  94342462.00   \n",
       "\n",
       "                amount      rate     rate2  \n",
       "date                                        \n",
       "20050104  4.431977e+06       NaN       NaN  \n",
       "20050105  4.529208e+06  0.009941  0.009941  \n",
       "20050106  3.921015e+06 -0.009460 -0.009460  \n",
       "20050107  4.737469e+06  0.000797  0.000797  \n",
       "20050110  3.762933e+06  0.010083  0.010083  \n",
       "...                ...       ...       ...  \n",
       "20220919  1.835744e+08 -0.001191 -0.001191  \n",
       "20220920  1.792482e+08  0.001231  0.001231  \n",
       "20220921  1.689391e+08 -0.007400 -0.007400  \n",
       "20220922  1.611341e+08 -0.008810 -0.008810  \n",
       "20220923  1.729048e+08 -0.003443 -0.003443  \n",
       "\n",
       "[4309 rows x 9 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['rate2'] = df['close'] / df['close'].shift(1) -1 # shift往“下”移一位\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "b3ad9bd8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-0.723\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c071ca7748>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#1元波动图\n",
    "df['equity'] = (df['rate'] +1).cumprod()\n",
    "#max_equity 为有史以来的最大值序列\n",
    "df['max_equity'] = df['equity'].expanding().max()\n",
    "df['max_rolling'] = df['equity'].rolling(200).max()\n",
    "mdd = round((df['equity'] / df['equity'].expanding(min_periods=1).max()).min() - 1, 3)\n",
    "print(mdd)\n",
    "df[['equity','max_equity','max_rolling']].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "91c1518c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    4308.000000\n",
       "mean        0.000456\n",
       "std         0.016635\n",
       "min        -0.092398\n",
       "25%        -0.007149\n",
       "50%         0.000798\n",
       "75%         0.008560\n",
       "max         0.093420\n",
       "Name: rate, dtype: float64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['rate'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "ba665836",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date\n",
       "20050104     983.7940\n",
       "20050105     993.5640\n",
       "20050106     984.1740\n",
       "20050107     984.9580\n",
       "20050110     994.8790\n",
       "              ...    \n",
       "20220919    3929.0001\n",
       "20220920    3933.8361\n",
       "20220921    3904.7348\n",
       "20220922    3870.3440\n",
       "20220923    3857.0212\n",
       "Length: 4309, dtype: float64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.apply(lambda x:x[\"close\"]+1, axis=1)"
   ]
  },
  {
   "cell_type": "raw",
   "id": "d9852409",
   "metadata": {},
   "source": [
    "两个及以上的时间序列处理\n",
    "这个的应用场景是把“回归”问题，变成“分类”问题，这在机器学习中比较重要。比如收益率，是一个连续型变量，如果要用“分类”模型来解决，需要把收益率序列进行分组。\n",
    "\n",
    "cut是把按值来划分，比如20岁以下叫少年，60岁以上叫老人等。\n",
    "\n",
    "qcut是按分位点来等，比如前10%，中间20%，后70%等。\n",
    "\n",
    "在实际应用中，我们按分位点来等分会比较多。比如0，0.2，0.4，0.6，0.8，1， 这样把收益率进行5等分。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "cb1848eb",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['婴儿', '婴儿', '青年', '壮年', '壮年', ..., '青年', '青年', '中年', '中年', '壮年']\n",
      "Length: 16\n",
      "Categories (5, object): ['婴儿' < '青年' < '中年' < '壮年' < '老年']\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0      small number\n",
       "1      large number\n",
       "2      small number\n",
       "3      large number\n",
       "4     middle number\n",
       "5      large number\n",
       "6      small number\n",
       "7      large number\n",
       "8      large number\n",
       "9      large number\n",
       "10     large number\n",
       "dtype: category\n",
       "Categories (3, object): ['small number' < 'middle number' < 'large number']"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "ages = np.array([1,5,10,40,36,12,58,62,77,89,100,18,20,25,30,32]) #年龄数据\n",
    "cut_ages = pd.cut(ages, [0,5,20,30,50,100], labels=[u\"婴儿\",u\"青年\",u\"中年\",u\"壮年\",u\"老年\"])\n",
    "print(cut_ages)\n",
    "\n",
    "data = pd.Series([0,8,1,5,3,7,2,6,10,4,9])\n",
    "pd.qcut(data,[0,0.2,0.3,1],labels=['small number','middle number','large number'])"
   ]
  },
  {
   "cell_type": "raw",
   "id": "dd40f62e",
   "metadata": {},
   "source": [
    "rolling滑动窗口​，在量化回测中比较重要。\n",
    "rolling会取指定窗口的数据，min_periods决定​最少需要几个数据及以上才进行计算。\n",
    "rolling().max()和rolling().min()在“海龟策略”里就有​实际应用。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "4fac5cdc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0      NaN\n",
      "1      NaN\n",
      "2    300.0\n",
      "3    350.0\n",
      "4    370.0\n",
      "5    390.0\n",
      "6    320.0\n",
      "7    300.0\n",
      "8    270.0\n",
      "9    340.0\n",
      "dtype: float64\n",
      "0    100.0\n",
      "1    190.0\n",
      "2    300.0\n",
      "3    350.0\n",
      "4    370.0\n",
      "5    390.0\n",
      "6    320.0\n",
      "7    300.0\n",
      "8    270.0\n",
      "9    340.0\n",
      "dtype: float64\n",
      "0           NaN\n",
      "1           NaN\n",
      "2    100.000000\n",
      "3    116.666667\n",
      "4    123.333333\n",
      "5    130.000000\n",
      "6    106.666667\n",
      "7    100.000000\n",
      "8     90.000000\n",
      "9    113.333333\n",
      "dtype: float64\n",
      "0    100.000000\n",
      "1     95.000000\n",
      "2    100.000000\n",
      "3    116.666667\n",
      "4    123.333333\n",
      "5    130.000000\n",
      "6    106.666667\n",
      "7    100.000000\n",
      "8     90.000000\n",
      "9    113.333333\n",
      "dtype: float64\n",
      "=============max===================\n",
      "0      NaN\n",
      "1      NaN\n",
      "2    110.0\n",
      "3    150.0\n",
      "4    150.0\n",
      "5    150.0\n",
      "6    130.0\n",
      "7    130.0\n",
      "8    100.0\n",
      "9    150.0\n",
      "dtype: float64\n",
      "0    100.0\n",
      "1    100.0\n",
      "2    110.0\n",
      "3    150.0\n",
      "4    150.0\n",
      "5    150.0\n",
      "6    130.0\n",
      "7    130.0\n",
      "8    100.0\n",
      "9    150.0\n",
      "dtype: float64\n",
      "=============min===================\n",
      "0      NaN\n",
      "1      NaN\n",
      "2     90.0\n",
      "3     90.0\n",
      "4    110.0\n",
      "5    110.0\n",
      "6     80.0\n",
      "7     80.0\n",
      "8     80.0\n",
      "9     90.0\n",
      "dtype: float64\n",
      "0    100.0\n",
      "1     90.0\n",
      "2     90.0\n",
      "3     90.0\n",
      "4    110.0\n",
      "5    110.0\n",
      "6     80.0\n",
      "7     80.0\n",
      "8     80.0\n",
      "9     90.0\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "amount = pd.Series([100, 90, 110, 150, 110, 130, 80, 90, 100, 150])\n",
    "print(amount.rolling(3).sum())\n",
    "print(amount.rolling(3,min_periods=1).sum())\n",
    "print(amount.rolling(3).mean())\n",
    "print(amount.rolling(3,min_periods=1).mean())\n",
    "print(\"=============max===================\")\n",
    "print(amount.rolling(3).max())\n",
    "print(amount.rolling(3,min_periods=1).max())\n",
    "\n",
    "print(\"=============min===================\")\n",
    "print(amount.rolling(3).min())\n",
    "print(amount.rolling(3,min_periods=1).min())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "0cb42d28",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          000300.SH\n",
      "date               \n",
      "20050104        NaN\n",
      "20050105   1.009941\n",
      "20050106   1.000387\n",
      "20050107   1.001184\n",
      "20050110   1.011279                SPX\n",
      "date              \n",
      "20061016       NaN\n",
      "20061017  0.996348\n",
      "20061018  0.997743\n",
      "20061019  0.998473\n",
      "20061020  0.999671\n"
     ]
    }
   ],
   "source": [
    "def load_data(name):\n",
    "    df = pd.read_csv('../data/csv/{}.csv'.format(name))\n",
    "    df['date'] = df['date'].apply(lambda x:str(x))\n",
    "    df.set_index('date',inplace=True)\n",
    "    df.sort_index(ascending=True,inplace=True)\n",
    "    df[name] = (1+df['close'].pct_change()).cumprod()\n",
    "    df = df[[name]]\n",
    "    #df.rename(columns={'close':name}, inplace=True)\n",
    "    return df\n",
    "df_300 = load_data('000300.SH')\n",
    "df_spx = load_data('SPX')\n",
    "print(df_300.head(),df_spx.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "5ae88cba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>000300.SH</th>\n",
       "      <th>SPX</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20050104</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20050105</th>\n",
       "      <td>1.009941</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20050106</th>\n",
       "      <td>1.000387</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20050107</th>\n",
       "      <td>1.001184</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20050110</th>\n",
       "      <td>1.011279</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20220919</th>\n",
       "      <td>3.996768</td>\n",
       "      <td>2.848610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20220920</th>\n",
       "      <td>4.001689</td>\n",
       "      <td>2.816500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20220921</th>\n",
       "      <td>3.972078</td>\n",
       "      <td>2.768292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20220922</th>\n",
       "      <td>3.937085</td>\n",
       "      <td>2.744962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20220923</th>\n",
       "      <td>3.923529</td>\n",
       "      <td>2.697659</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4568 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          000300.SH       SPX\n",
       "date                         \n",
       "20050104        NaN       NaN\n",
       "20050105   1.009941       NaN\n",
       "20050106   1.000387       NaN\n",
       "20050107   1.001184       NaN\n",
       "20050110   1.011279       NaN\n",
       "...             ...       ...\n",
       "20220919   3.996768  2.848610\n",
       "20220920   4.001689  2.816500\n",
       "20220921   3.972078  2.768292\n",
       "20220922   3.937085  2.744962\n",
       "20220923   3.923529  2.697659\n",
       "\n",
       "[4568 rows x 2 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_all = pd.concat([df_300,df_spx], axis=1)\n",
    "df_all.sort_index(ascending=True, inplace=True)\n",
    "df_all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "92295402",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c070826788>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_all.plot()"
   ]
  },
  {
   "cell_type": "raw",
   "id": "8cf6dde0",
   "metadata": {},
   "source": [
    "思考题：如何把这两上dataframe对齐后进行比较。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "16a053ff",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
